{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " > Setting up Audio Processor...\n",
      " | > sample_rate:22050\n",
      " | > resample:False\n",
      " | > num_mels:80\n",
      " | > log_func:np.log10\n",
      " | > min_level_db:-100\n",
      " | > frame_shift_ms:None\n",
      " | > frame_length_ms:None\n",
      " | > ref_level_db:20\n",
      " | > fft_size:1024\n",
      " | > power:1.5\n",
      " | > preemphasis:0.0\n",
      " | > griffin_lim_iters:60\n",
      " | > signal_norm:True\n",
      " | > symmetric_norm:True\n",
      " | > mel_fmin:0\n",
      " | > mel_fmax:None\n",
      " | > pitch_fmin:1.0\n",
      " | > pitch_fmax:640.0\n",
      " | > spec_gain:20.0\n",
      " | > stft_pad_mode:reflect\n",
      " | > max_norm:4.0\n",
      " | > clip_norm:True\n",
      " | > do_trim_silence:True\n",
      " | > trim_db:45\n",
      " | > do_sound_norm:False\n",
      " | > do_amp_to_db_linear:True\n",
      " | > do_amp_to_db_mel:True\n",
      " | > do_rms_norm:False\n",
      " | > db_level:None\n",
      " | > stats_path:None\n",
      " | > base:10\n",
      " | > hop_length:256\n",
      " | > win_length:1024\n",
      " | > Found 13100 files in /maindata/data/user/ai_story/zhigong.wang/endless-frontier/simple-multimodal/tts/dataset/LJSpeech-1.1\n",
      "processor.load_wav= [-0.00167847 -0.00149536  0.00015259 ...  0.00091553  0.00183105\n",
      "  0.0012207 ] (127205,)\n",
      "tokenizer.text_to_ids= [64, 22, 130, 64, 28, 130, 110, 41, 112, 5, 24, 64, 49, 21, 130, 31, 29, 22, 130, 14, 111, 51, 7, 98, 49, 5, 110, 64, 14, 64, 82, 11, 130, 64, 28, 130, 31, 49, 130, 9, 110, 52, 112, 21, 22, 130, 116, 110, 64, 34, 130, 22, 49, 5, 11, 130, 110, 8, 64, 15, 7, 130, 29, 22, 130, 64, 16, 31, 49, 130, 9, 110, 44, 112, 78, 15, 28, 130, 92, 24, 31, 49, 130, 14, 110, 51, 82, 50, 28] 89\n",
      "processor.melspectrogram= (80, 497)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(13087,\n",
       " [{'text': 'Two young women,\\n',\n",
       "   'audio_file': 'dataset/LJSpeech-1.1/wavs/LJ037-0076.wav',\n",
       "   'speaker_name': 'ljspeech',\n",
       "   'root_path': 'dataset/LJSpeech-1.1/',\n",
       "   'language': '',\n",
       "   'audio_unique_name': 'ljspeech#wavs/LJ037-0076'},\n",
       "  {'text': 'On the contrary,\\n',\n",
       "   'audio_file': 'dataset/LJSpeech-1.1/wavs/LJ021-0165.wav',\n",
       "   'speaker_name': 'ljspeech',\n",
       "   'root_path': 'dataset/LJSpeech-1.1/',\n",
       "   'language': '',\n",
       "   'audio_unique_name': 'ljspeech#wavs/LJ021-0165'}])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from TTS.tts.configs.shared_configs import BaseDatasetConfig\n",
    "from TTS.tts.configs.glow_tts_config import GlowTTSConfig\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.tts.utils.text.tokenizer import TTSTokenizer\n",
    "from TTS.tts.datasets import load_tts_samples\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def init_config():\n",
    "    dataset_config = BaseDatasetConfig(dataset_name='ljspeech',\n",
    "                                       formatter='ljspeech',\n",
    "                                       path='dataset/LJSpeech-1.1/',\n",
    "                                       meta_file_train='metadata.csv')\n",
    "\n",
    "    config = GlowTTSConfig(\n",
    "        batch_size=32,\n",
    "        eval_batch_size=16,\n",
    "        num_loader_workers=4,\n",
    "        num_eval_loader_workers=4,\n",
    "        run_eval=True,\n",
    "        test_delay_epochs=-1,\n",
    "        epochs=10,\n",
    "        text_cleaner='phoneme_cleaners',\n",
    "        use_phonemes=True,\n",
    "        phoneme_language='en-us',\n",
    "        phoneme_cache_path='./models/train2/phoneme_cache',\n",
    "        print_step=25,\n",
    "        print_eval=False,\n",
    "        mixed_precision=True,\n",
    "        output_path='./models/train2',\n",
    "        datasets=[dataset_config],\n",
    "        save_step=1000,\n",
    "        data_dep_init_steps=0,\n",
    "    )\n",
    "\n",
    "    processor = AudioProcessor.init_from_config(config)\n",
    "    tokenizer, config = TTSTokenizer.init_from_config(config)\n",
    "\n",
    "    datas, _ = load_tts_samples(\n",
    "        dataset_config,\n",
    "        eval_split=True,\n",
    "        eval_split_size=0.001,\n",
    "    )\n",
    "\n",
    "    #排序\n",
    "    lens = [os.path.getsize(i['audio_file']) for i in datas]\n",
    "    ids = np.argsort(lens)\n",
    "    datas = [datas[i] for i in ids]\n",
    "\n",
    "    return config, processor, tokenizer, datas\n",
    "\n",
    "\n",
    "config, processor, tokenizer, datas = init_config()\n",
    "\n",
    "out = processor.load_wav('dataset/LJSpeech-1.1/wavs/LJ001-0108.wav')\n",
    "print('processor.load_wav=', out, out.shape)\n",
    "\n",
    "out = tokenizer.text_to_ids(\n",
    "    'it is obvious that legibility is the first thing to be aimed at in the forms of the letters'\n",
    ")\n",
    "print('tokenizer.text_to_ids=', out, len(out))\n",
    "\n",
    "out = processor.melspectrogram(\n",
    "    processor.load_wav('dataset/LJSpeech-1.1/wavs/LJ001-0108.wav'))\n",
    "print('processor.melspectrogram=', out.shape)\n",
    "\n",
    "len(datas), datas[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "> DataLoader initialization\n",
      "| > Tokenizer:\n",
      "\t| > add_blank: False\n",
      "\t| > use_eos_bos: False\n",
      "\t| > use_phonemes: True\n",
      "\t| > phonemizer:\n",
      "\t\t| > phoneme language: en-us\n",
      "\t\t| > phoneme backend: espeak\n",
      "| > Number of instances : 13087\n",
      " | > Preprocessing samples\n",
      " | > Max text length: 188\n",
      " | > Min text length: 13\n",
      " | > Avg text length: 100.92282417666387\n",
      " | \n",
      " | > Max audio length: 222643.0\n",
      " | > Min audio length: 24499.0\n",
      " | > Avg audio length: 145010.87774126997\n",
      " | > Num. instances discarded samples: 0\n",
      " | > Batch group size: 0.\n",
      "2861.0257\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(RAdam (\n",
       " Parameter Group 0\n",
       "     betas: [0.9, 0.998]\n",
       "     buffer: [[None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None], [None, None, None]]\n",
       "     eps: 1e-08\n",
       "     initial_lr: 0.001\n",
       "     lr: 2.5e-07\n",
       "     weight_decay: 1e-06\n",
       " ),\n",
       " <trainer.torch.NoamLR at 0x7f72ae7dee60>,\n",
       " GlowTTSLoss(),\n",
       " <torch.utils.data.dataloader.DataLoader at 0x7f728cd88ca0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from TTS.tts.models.glow_tts import GlowTTS\n",
    "from trainer import Trainer, TrainerArgs\n",
    "from TTS.utils.radam import RAdam\n",
    "from trainer.torch import NoamLR\n",
    "from TTS.tts.layers.losses import GlowTTSLoss\n",
    "\n",
    "\n",
    "def init_model(from_trainer):\n",
    "    model = GlowTTS(config, processor, tokenizer, speaker_manager=None)\n",
    "    model.run_data_dep_init = False\n",
    "\n",
    "    if from_trainer:\n",
    "        trainer = Trainer(args=TrainerArgs(),\n",
    "                          config=config,\n",
    "                          output_path='./models/train2',\n",
    "                          model=model,\n",
    "                          train_samples=datas,\n",
    "                          eval_samples=None)\n",
    "        optimizer = trainer.get_optimizer(model, config)\n",
    "        scheduler = trainer.get_scheduler(model, config, optimizer)\n",
    "        criterion = trainer.get_criterion(model)\n",
    "        loader = trainer.get_train_dataloader({}, datas, verbose=True)\n",
    "    else:\n",
    "        optimizer = RAdam(model.parameters(),\n",
    "                          lr=1e-3,\n",
    "                          betas=[0.9, 0.998],\n",
    "                          weight_decay=1e-6)\n",
    "        scheduler = NoamLR(optimizer, warmup_steps=4000)\n",
    "        criterion = GlowTTSLoss()\n",
    "        loader = model.get_data_loader(config=config,\n",
    "                                       assets={},\n",
    "                                       is_eval=False,\n",
    "                                       samples=datas,\n",
    "                                       verbose=True,\n",
    "                                       num_gpus=0)\n",
    "\n",
    "    return model, optimizer, scheduler, criterion, loader\n",
    "\n",
    "\n",
    "model, optimizer, scheduler, criterion, loader = init_model(from_trainer=False)\n",
    "\n",
    "#统计参数量\n",
    "print(sum(i.numel() for i in model.parameters()) / 10000)\n",
    "\n",
    "optimizer, scheduler, criterion, loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 4.961163520812988 2.5e-07\n",
      "0 50 4.341901779174805 2.5e-07\n",
      "0 100 4.273308277130127 2.5e-07\n",
      "0 150 4.2002716064453125 2.5e-07\n",
      "0 200 4.3335676193237305 2.5e-07\n",
      "0 250 4.196634292602539 2.5e-07\n",
      "0 300 4.177802085876465 2.5e-07\n",
      "0 350 4.187454700469971 2.5e-07\n",
      "0 400 4.069185256958008 2.5e-07\n",
      "1 0 4.796741485595703 2.5e-07\n",
      "1 50 4.15220308303833 2.5e-07\n",
      "1 100 4.13557243347168 2.5e-07\n",
      "1 150 3.998579502105713 2.5e-07\n",
      "1 200 4.026664733886719 2.5e-07\n",
      "1 250 3.866764545440674 2.5e-07\n",
      "1 300 3.8201279640197754 2.5e-07\n",
      "1 350 3.8089382648468018 2.5e-07\n",
      "1 400 3.7218315601348877 2.5e-07\n",
      "2 0 4.497714996337891 5e-07\n",
      "2 50 3.812983274459839 5e-07\n",
      "2 100 3.5939581394195557 5e-07\n",
      "2 150 3.489596128463745 5e-07\n",
      "2 200 3.395341634750366 5e-07\n",
      "2 250 3.203160285949707 5e-07\n",
      "2 300 3.0764355659484863 5e-07\n",
      "2 350 3.001601219177246 5e-07\n",
      "2 400 2.8414101600646973 5e-07\n",
      "3 0 3.6368300914764404 7.5e-07\n",
      "3 50 2.8385250568389893 7.5e-07\n",
      "3 100 2.8441545963287354 7.5e-07\n",
      "3 150 2.7014265060424805 7.5e-07\n",
      "3 200 2.578706741333008 7.5e-07\n",
      "3 250 2.7139430046081543 7.5e-07\n",
      "3 300 2.6471023559570312 7.5e-07\n",
      "3 350 2.5833308696746826 7.5e-07\n",
      "3 400 2.5595149993896484 7.5e-07\n",
      "4 0 2.8775289058685303 1e-06\n",
      "4 50 2.547891616821289 1e-06\n",
      "4 100 2.4772391319274902 1e-06\n",
      "4 150 2.4320316314697266 1e-06\n",
      "4 200 2.3750288486480713 1e-06\n",
      "4 250 2.3566393852233887 1e-06\n",
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      "4 350 2.2114996910095215 1e-06\n",
      "4 400 2.1881368160247803 1e-06\n",
      "5 0 2.384719133377075 1.2499999999999999e-06\n",
      "5 50 2.1242032051086426 1.2499999999999999e-06\n",
      "5 100 2.0794479846954346 1.2499999999999999e-06\n",
      "5 150 2.047539234161377 1.2499999999999999e-06\n",
      "5 200 1.977477788925171 1.2499999999999999e-06\n",
      "5 250 1.955277681350708 1.2499999999999999e-06\n",
      "5 300 1.8787392377853394 1.2499999999999999e-06\n",
      "5 350 1.8750362396240234 1.2499999999999999e-06\n",
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      "6 0 1.9948418140411377 1.5e-06\n",
      "6 50 1.799180269241333 1.5e-06\n",
      "6 100 1.781996726989746 1.5e-06\n",
      "6 150 1.7778515815734863 1.5e-06\n",
      "6 200 1.74971342086792 1.5e-06\n",
      "6 250 1.7661329507827759 1.5e-06\n",
      "6 300 1.6990951299667358 1.5e-06\n",
      "6 350 1.7191340923309326 1.5e-06\n",
      "6 400 1.7099764347076416 1.5e-06\n",
      "7 0 1.8319087028503418 1.75e-06\n",
      "7 50 1.6724504232406616 1.75e-06\n",
      "7 100 1.6803032159805298 1.75e-06\n",
      "7 150 1.6642913818359375 1.75e-06\n",
      "7 200 1.6340398788452148 1.75e-06\n",
      "7 250 1.6661311388015747 1.75e-06\n",
      "7 300 1.6136841773986816 1.75e-06\n",
      "7 350 1.6193482875823975 1.75e-06\n",
      "7 400 1.593254566192627 1.75e-06\n",
      "8 0 1.717733383178711 2e-06\n",
      "8 50 1.5594158172607422 2e-06\n",
      "8 100 1.5504382848739624 2e-06\n",
      "8 150 1.530715823173523 2e-06\n",
      "8 200 1.5161908864974976 2e-06\n",
      "8 250 1.5233244895935059 2e-06\n",
      "8 300 1.482980728149414 2e-06\n",
      "8 350 1.481778860092163 2e-06\n",
      "8 400 1.4647119045257568 2e-06\n",
      "9 0 1.5963149070739746 2.25e-06\n",
      "9 50 1.4304654598236084 2.25e-06\n",
      "9 100 1.436292052268982 2.25e-06\n",
      "9 150 1.4040786027908325 2.25e-06\n",
      "9 200 1.3996793031692505 2.25e-06\n",
      "9 250 1.4092414379119873 2.25e-06\n",
      "9 300 1.3749940395355225 2.25e-06\n",
      "9 350 1.374279499053955 2.25e-06\n",
      "9 400 1.3542451858520508 2.25e-06\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "def train():\n",
    "    global model\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "    model.train()\n",
    "    model = model.to(device)\n",
    "\n",
    "    for epoch in range(config.epochs):\n",
    "        for i, data in enumerate(loader):\n",
    "            data = model.format_batch(data)\n",
    "            for k in data.keys():\n",
    "                if isinstance(data[k], torch.Tensor):\n",
    "                    data[k] = data[k].to(device)\n",
    "\n",
    "            _, loss_dict = model.train_step(data, criterion)\n",
    "\n",
    "            model.zero_grad(set_to_none=True)\n",
    "            loss_dict['loss'].backward()\n",
    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad(set_to_none=True)\n",
    "\n",
    "            if i % 50 == 0:\n",
    "                lr = optimizer.state_dict()['param_groups'][0]['lr']\n",
    "                print(epoch, i, loss_dict['loss'].item(), lr)\n",
    "\n",
    "        scheduler.step()\n",
    "\n",
    "    config.save_json('models/train2/config.json')\n",
    "    model = model.cpu()\n",
    "    torch.save({\n",
    "        'config': config.to_dict(),\n",
    "        'model': model.state_dict()\n",
    "    }, 'models/train2/model.pth')\n",
    "\n",
    "\n",
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " > Using model: glow_tts\n",
      " > Setting up Audio Processor...\n",
      " | > sample_rate:22050\n",
      " | > resample:False\n",
      " | > num_mels:80\n",
      " | > log_func:np.log10\n",
      " | > min_level_db:-100\n",
      " | > frame_shift_ms:None\n",
      " | > frame_length_ms:None\n",
      " | > ref_level_db:20\n",
      " | > fft_size:1024\n",
      " | > power:1.5\n",
      " | > preemphasis:0.0\n",
      " | > griffin_lim_iters:60\n",
      " | > signal_norm:True\n",
      " | > symmetric_norm:True\n",
      " | > mel_fmin:0\n",
      " | > mel_fmax:None\n",
      " | > pitch_fmin:1.0\n",
      " | > pitch_fmax:640.0\n",
      " | > spec_gain:20.0\n",
      " | > stft_pad_mode:reflect\n",
      " | > max_norm:4.0\n",
      " | > clip_norm:True\n",
      " | > do_trim_silence:True\n",
      " | > trim_db:45\n",
      " | > do_sound_norm:False\n",
      " | > do_amp_to_db_linear:True\n",
      " | > do_amp_to_db_mel:True\n",
      " | > do_rms_norm:False\n",
      " | > db_level:None\n",
      " | > stats_path:None\n",
      " | > base:10\n",
      " | > hop_length:256\n",
      " | > win_length:1024\n",
      " > Text splitted to sentences.\n",
      "['a quick brown fox jumps over the lazy dog']\n",
      " > Processing time: 0.5648984909057617\n",
      " > Real-time factor: 0.3473898852206617\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from TTS.utils.synthesizer import Synthesizer\n",
    "import IPython\n",
    "\n",
    "synthesizer = Synthesizer(\n",
    "    tts_checkpoint='./models/train2/model.pth',\n",
    "    tts_config_path='./models/train2/config.json',\n",
    "    use_cuda=False,\n",
    ")\n",
    "\n",
    "synthesizer.save_wav(synthesizer.tts('a quick brown fox jumps over the lazy dog'), 'out4.wav')\n",
    "\n",
    "IPython.display.Audio('out4.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "simple",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
